Five big data trends forecasted for 2019

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Various industry domains are generating an immense amount of data and as a result the amount of data in existence demands for big data solutions to be in place in for storage and management. Big data architectures are set up to streamline and maintain the datasets. As today’s businesses have become data-driven, big data services are extremely sought after.

In the incubation stages, big data was deployed on by businesses that were massive in scale. Only these organizations could afford the technology, skilled manpower and channels to store, collect and analyze the data. With time, the scope of big data services has also changed. Organizations from enterprises to small-scale organizations are in need of big data solutions to obtain intelligent business insights from their data – big data extending itself to the cloud has allowed even small businesses to access and take advantage of it.

The information in terms of unstructured data is a never-ending stream. It is good to have all that data at your disposal. However, it will prove to be a challenge if you have all that unstructured data and are not able to obtain actionable insights from it. This is where big data presents tremendous opportunities for business growth.

Listed below are the top five big data trends in 2019:

Quantum computing

Quantum computing is the next big thing in the world of big data and analytics. Even with the existing technologies ta hand, analyzing and interpreting massive datasets can be challenging and times and may even be time-consuming. Reduction in processing time and the ability to make timely decisions for better results can be achieved through quantum computing. Tech giants like Google and IBM are on their way to building the world’s first quantum computer. 1QB is a company that already is the first quantum computing software company. Data encryption, real-time solutions and complex problem solving can be done efficiently.

The Internet of Things

The fastest growing trend right now is the Internet of Things (IoT). By the end of 2020, IoT will generate close to $300bn in revenue. IoT is not specific just to smartphones. Google home or Alexa is an example of an IoT. But why does all this matter to big data? This is because of the amount of data that is generated via all these devices. Tracking patterns, trends, performing customer analysis, product improvement, so on and so forth can be done by structuring these large data sets to obtain actionable insights.

Predictive analytics

Predictive analytics has been around for a while now. However, organizations are only now realizing its true potential. As an example, predicting consumer behavior helps companies come up with recommendations and gives them an opportunity to cross-sell. It can also be used to predict equipment failure in manufacturing and predict the lifeline of medical equipment in the healthcare industry.

Dark data rises

The next stage from the after big data solutions are in place is to analyze the streamlined data. Even today, after emphasizing on the importance of data, there are many companies that have data that can’t be used for analysis, to make decisions or decipher insights – this data is termed as "dark data". With the rise of big data analytics, these analog databases can be converted into a digital form and can be migrated to the cloud for analysis. This can eliminate the chance of data being lost and will mitigate any potential security risk from the dark data.

Open source data tools over licensed

What we saw come to light during the second half of 2018, will take off big time in 2019 – the rise of open source tools. Free software and open source data tools will be available on the cloud. Many more tools like Hadoop, Elasticsearch and Cassandra will come to the fore. These tools will be upgrades to the existing ones. Open source tools are a lot cheaper than the licensed ones. This will make it affordable and will allow small and medium-size enterprises to gain immense benefits from these big data technologies.

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